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We've paid these famous graphs our sincerest form of compliment: trying to re-make them for the web. This post is inspired by <a href="http://www.edwardtufte.com/tufte/">Edward Tufte</a>. The plots were made with our free online product--you can press "play with this data" and start editing your own copy online. We have <a href="https://plot.ly/learn">tutorials</a> for using our web product and <a href="https://plot.ly/api">APIs</a>; contact us if you're interested in a trial of <a href="https://plot.ly/product/enterprise/">plotly on-premise</a>. 
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<h3>March on Moscow</h3>
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<a href="http://www.edwardtufte.com/tufte/minard-obit">Charles Minard</a>'s graph of the March on Moscow shows the dwindling size of Napoleon's army. The broad tan line shows the army's size on the March from Poland to Moscow. The lower, thinner, dark line shows the army's size on the retreat. The width of the lines shows the army size, which started over 400,000 strong and dwindled to 10,000. The bottom lines show t

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install.packages("devtools")  # so we can install from github
library("devtools")
install_github("ropensci/plotly")  # plotly is part of ropensci
library(plotly)

py <- plotly(username="r_user_guide", key="mw5isa4yqp")  # open plotly connection

library(ggplot2)
d <- 1:9
prob = log10(1 + (1/d))

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NMDS1   NMDS2   shannon
-0.287555952    -0.129887595    9.516558582
-0.314104852    -0.048655648    8.985087924
-0.214910534    -0.127167065    8.928241917
-0.341295065    -0.296282805    8.315476782
-0.470025718    0.083835083 8.494348157
-0.429386114    0.044064347 8.669813919
-0.427608469    0.124631936 8.15886319
-0.584412991    0.257278736 8.469688185
-0.436526047    -0.070633108    8.496878956

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install.packages("devtools")  # so we can install from github
library("devtools")
install_github("ropensci/plotly")  # plotly is part of ropensci
library(plotly)
 
py <- plotly(username="r_user_guide", key="mw5isa4yqp")  # open plotly connection

library(ggplot2)
anscombe_m <- data.frame()

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Let's say you want to visually compare the peaks in a mountain range. Or you need to describe irregularities in temperature over time. But all your data is in a spreadsheet. How would you do it?
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A map. Not just any map. A beautiful, interactive, online map.
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<br>
This post shows how to use interactivity and maps in <a href="https://plot.ly">Plotly</a>, a new graphing and analytics platform. Our <a href="https://plot.ly/api">APIs</a> for <a href="https://plot.ly/r">R</a>, <a href="https://plot.ly/python">Python</a>, and <a href="https://plot.ly/matlab">MATLAB</a> let you collaboratively make and share plots and maps. You can make plots using <a href="https://plot.ly/ggplot2">ggplot2</a>, <a href="https://plot.ly/matplotlib">matplotlib</a>, Excel, <a href="https://github.com/plotly/Igor-Pro-Graph-Converter">Igor Pro</a>, or our web app--entirely with coding or without any. Your call.
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<br>
<h2>1. Canadian Cities, Virginia Breweries, & Election Maps</h2>

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